Model based certainty metric is a data-independent approach derived from prior work on Confident Learning (CI) [1]}https://github.com/cleanlab/cleanlab, and automatically learns a class-conditional joint distribution (Q) between provided dataset labels (assumed noisy) and the latent labels (assumed uncorrupted), to identify noisy samples. This approach presumes each class may be independently mislabelled as an incorrect class with a probability that is data-independent. At inference, Q is used to compute a model certainty (\(\delta \) ) score for every sample, and the top 90 percentile with highest scores are marked as noisy/mislabelled.
